It is often desirable to monitor operations over a large area, such as a railroad yard, an airport, an industrial loading area, and/or the like, where mistakes or failures during the operations can lead to serious consequences. Current approaches for monitoring operations in such areas utilize one of two basic approaches. By far the most common, which is currently in use in most rail settings, airports, and similar locations, uses human “spotters.” A spotter is a person whose sole or partial function is to observe the operations of the area and recognize abnormal or dangerous situations. While, in theory, this is the best possible solution—given the capability of human beings to recognize patterns, analyze scenes, and so on—in practice it is well known that human-based monitoring has several major limitations.
These limitations include physical limitations of human perception, based on illumination and contrast, which make, for example, a rail yard at night extremely difficult to fully perceive (glaring lights, shadowed areas, etc.) and can thus lead to a failure of the observer to actually see the problem event or object. To this extent, physical failures, such as the loss of an illuminating light, can drastically reduce the capability of any human being to operate in these conditions, and other lights (for instance, powerful headlights) can cause glare that blinds human observers to any objects in the vicinity. Additionally, human beings involved in an observation-based task, which involves a lot of routine and very infrequent situations of interest, are also well-known to lose their perceptual edge and fail to be actually as attentive as they should be, thus missing key events. In addition, urgency, excitement, boredom, or other psychological factors can lead a human observer to directly misperceive an event, deciding that something is perfectly normal when in fact it is not.
A few prior art approaches have been developed to observe generally more limited areas. For example, the Autoscope system, offered by ImageSensing Systems, Inc., is designed for monitoring roadway operations. While these units are generally supplied with basic levels of computation and image processing (thresholding, blob detection, etc.) the units are limited to single spectrum operation, do not make decisions themselves, and in fact pass all data from the monitoring units in the area to a single central processor which does all of the complex work of scene understanding and makes any key decisions on alerts or actions to be taken. Autoscope, and similar systems, base decisions on individual images. Moreover, the systems as designed are inadequate for monitoring larger areas and rely purely on a fairly simple geometry to work; they do not actually understand the three-dimensional geometry that is inherent to a complex scene.